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main_script_production_9.R
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main_script_production_9.R
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#################################################################################
###
### Make sure I start with a clean slate.
### Set the directory to the latest working directory.
###
###
### Do not run the rm command often.
###
rm(list=ls(all=TRUE))
setwd("C:/Users/proud/Documents/RESEARCH/PROJECT - Superconductivity/Productions/Production_9")
#################################################################################
###
### This part describes the clean up process for the data.
### The superconducting material list was obtained from:
### http://supercon.nims.go.jp/index_en.html
### on July 24th, 2017, 11:15 AM.
###
### Once logged in, I clicked on "OXIDE & METALLIC".
### The site took me to http://supercon.nims.go.jp/supercon/material_menu.
### There, I clicked on the "search" to get *all* the data.
### I took a screen shot of the site. The screen shot file is in "menu.pdf".
###
### 31639 rows of data are indicated.
###
### I clicked on "Data Download" to download the data.
### The file is "material.csv".
###
### I made a copy of "material.csv" into "material_copy.csv".
### "material_copy.csv" is the file used for all the clean up.
###
###
### The following are done manually:
### (1) Removed columns ma1 to mj2.
### (2) Sorted the data by Tc from highest to lowest.
### (3) The critical temp for the following "num" variables were shifted by ONE column to the
### right. I fixed these manually: 31020, 31021, 31022, 31023, 31024, 31025,
### 153150, 153149, 42170, 42171, 30716, 30717, 30718, 30719,
### 150001, 150002, 150003, 150004, 150005, 150006, 150007,
### 30712, 30713, 30714, 30715.
### (4) The following are removed since the cricial temp seem wrong:
### num = 111620, La0.23Th0.77Pb3
### num = 9632, Pb2C1Ag2O6
### num = 140, Er1Ba2Cu3O7-X
### (5) All rows with Tc = 0 or missing are removed.
### (6) Columns nums, mo1, mo2, oz, str3, tcn, tcfig, refno are removed.
### (7) The following changes are made manually:
### Y2C2Br0.5!1.5 is removed.
### Y1Ba2Cu3O6050 is removed. I suspect 6050 is a mistake.
### Nd185Ce0.15Cu1O4 is removed. There was a Nd1.85Ce0.15Cu1O4 already in the data.
### Hg1234O10 is removed. 1234 for Hg???? This doesnot seem correct.
### Bi1.6Pb0.4Sr2Cu3Ca2O1013 is changed to Bi1.6Pb0.4Sr2Cu3Ca2O10.13
### Y1Ba2Cu285Ni0.15O7 is changed to Y1Ba2Cu2.85Ni0.15O7
###
### (9) The following are removed because they have a zero as a coefficient:
### "Bi1.7Pb0.3Sr2Ca1Cu2O0" (Zero of Oxygen?)
### "La1.85Nd0Ca1.15Cu2O5.99"
### "Bi0Mo0.33Cu2.67Sr2Y1O7.41"
### "Y0.5Yb0.5Ba2Sr0Cu3O7"
### (10) These are removed because they don't make sense:
### Yo975Yb0.025Ba2Cu3O
### Yo975Yb0.025Ba2Cu3O
### Yo975Yb0.025Ba2Cu3O
### (10) Change all oxygen designations such as O8-z to O8, O5+X TO O5, etc.
### Important: I had to do this in the correct sequence; for example find and replace
### Oz+8 before Oz, etc.
### I did this manually! Took a lot of time. I initially approached it by
### string manipulations but trying to take care of the exceptions was taking too much time.
### (11) The column heading changed to "material" and critical_temp
###
###
### This is the fantastic R package that reads and breaks up formulas:
###
install.packages("CHNOSZ")
library(CHNOSZ)
###
### Now upload the data and make sure you get no error messages.
###
dat = read.csv("material_copy.csv", stringsAsFactors = FALSE, header = TRUE)
###
### The following takes about 9 seconds:
###
### Run to see if you see any problems: errors, warning, etc.
### Also gives you an idea about how long looping over 25000 rows
### and using the "makeup" function take.
###
system.time({
for (i in 1:nrow(dat))
{
makeup(dat$material[i])
}
})
###
### I got no error or warning messages.
###
###
### Just look at a snippet of the data:
###
head(dat)
summary(dat)
dim(dat)
###
### No missing values for critical temperatures.
### There are 24861 rows.
###
###
### Let's see how many rows were left out from the original data:
###
tmp = read.csv("material.csv", na.strings = "",
stringsAsFactors = FALSE,
header = TRUE)
dim(tmp)
###
### 31639 - 24861 = 6778 are left out.
###
###
### Clean up:
###
ls()
rm(tmp)
rm(i)
###
### Only the following elements will be considered.
### All the atomic numbers are less than 86.
###
elements = c("H", "He", "Li", "Be", "B", "C", "N", "O", "F", "Ne", "Na", "Mg",
"Al", "Si", "P", "S", "Cl", "Ar", "K", "Ca", "Sc", "Ti", "V", "Cr",
"Mn", "Fe", "Co", "Ni", "Cu", "Zn", "Ga", "Ge", "As", "Se", "Br", "Kr",
"Rb", "Sr", "Y", "Zr", "Nb", "Mo", "Tc", "Ru", "Rh", "Pd", "Ag", "Cd", "In",
"Sn", "Sb", "Te", "I", "Xe", "Cs", "Ba", "La", "Ce", "Pr", "Nd", "Pm", "Sm",
"Eu", "Gd", "Tb", "Dy", "Ho", "Er", "Tm", "Yb", "Lu", "Hf", "Ta", "W", "Re",
"Os", "Ir", "Pt", "Au", "Hg", "Tl", "Pb", "Bi", "Po", "At", "Rn")
###
### In this next section, I'll only pick up the materials with atomic number <= 86.
###
###
### The reject object holds the index value of the materials that are rejected.
###
### The following chunk takes about 9 seconds:
###
reject = rep(0, nrow(dat))
system.time({
for (i in 1:nrow(dat))
{
tmp = makeup(dat$material[i])
if (!all(names(tmp) %in% elements)) reject[i] = 1
}
})
###
### How many are rejected?
###
length(which(reject == 1))
###
### 973 are rejected.
###
###
### Pick up the remaining and save data into
### an object called kept_dat.
###
kept_dat = dat[ which(reject == 0), ]
dim(kept_dat)
dim(dat)
###
### Now I have 23888 = 24861 - 973 rows.
###
###
### Save the rejected material for later analysis.
###
rejected_dat = dat[ which(reject == 1), ]
###
### Again, some clean up.
###
ls()
rm(i)
rm(tmp)
###
### The following chunk will take about 265 seconds or about 5 minutes.
###
### Matrix (and later converted to data frame) m will be used later to
### gather summary statistics from the data.
### It could also be used for feature extraction.
###
row_names_kept_dat = row.names(kept_dat)
m = matrix(0, nrow(kept_dat), length(elements) )
colnames(m) = elements
system.time({
for ( i in 1:nrow(kept_dat) )
{
tmp_values = makeup(kept_dat$material[i])
tmp_location_of_matches = match( names(tmp_values), colnames(m) )
m[i, tmp_location_of_matches ] = tmp_values
}
})
###
### Add the temperature back:
###
m = as.data.frame(m)
m$critical_temp = kept_dat$critical_temp
###
### Add the row names back:
###
row.names(m) = row_names_kept_dat
###
### Get only unique rows.
###
dim(m)
unique_m = unique(m)
dim(unique_m)
###
### 23888 - 21263 = 2625 rows omitted.
###
###
### Now add the chemical formulas back to m:
###
m$material = kept_dat$material
###
### Now add the chemical formula's back to unique_m
###
unique_m$material = m$material[row.names(m) %in% row.names(unique_m)]
dim(m)
dim(unique_m)
###
### m: You have 23888 by 2
### unique_m: You have 21263 by 2
###
### 23888 - 21263 = 2625 duplicates
###
###
### I did a final check here:
###
### Here v just records the largest number of atoms
### for an element in a material.
### The intent is to catch any thing suspecious.
###
v = numeric(nrow(unique_m))
for (i in 1:nrow(unique_m))
{
v[i] = max(makeup(unique_m$material[i]))
}
unique_m$material[which(v > 90)]
###
### Most of these look legit but some don't look right. For example, "B105" doesn't look legit.
### They are mostly alloys.
### I will leave these as is.
###
dim(unique_m)
###
### I am down to 21263 material.
### I lost 31639 - 21263 = 10376 rows.
### I have picked up 67% of the data.
###
###
### Remove junk:
###
ls()
rm(i)
rm("row_names_kept_dat")
rm("tmp_location_of_matches")
rm("tmp_values")
rm(v)
ls()
gc()
###
### Next, I upload the data from elements.
### The subfolder has "Production_1" because this data never changed.
###
element_data = read.csv("C:/Users/proud/Documents/RESEARCH/PROJECT - Superconductivity/Productions/Production_1/data_csv.csv",header = T)
###
### Assign row names:
###
rownames(element_data) = element_data$Name
###
### Remove some useless stuff or stuff that can be highly
### correlated with other material.
### This was a judgement call and comes from initial experimentation
### with the element superconductivity.
###
element_data$Name = NULL
element_data$Color = NULL
element_data$ElectricalType = NULL
element_data$QuantumNumbers = NULL
element_data$RefractiveIndex = NULL
element_data$VanDerWaalsRadius = NULL
element_data$VickersHardness = NULL
element_data$BulkModulus = NULL
element_data$NeutronCrossSection = NULL
element_data$NeutronMassAbsorption = NULL
###
### This is what I have at this point:
###
### (1) dat: Data file that has the material, and the critical temperature.
### This is the cleaned data but has *duplicates* and materials with
### elements which could have atomic number over 86.
### (2) rejected_dat : This is the data which had elements with atomic number over 86.
### (8) reject: Index of rejected material in "dat" object.
### (3) kept_dat: This is the dat but with reject_data removed. It has duplicates.
### (2) element_data: This is the original file that has all the elements
### and their properties. This does have a lot of missing values.
### (3) elements: This is just a list of chemical symbols of elements used for the
### analysis.
### (6) m: This is a large data table that in addition to the material names
### and critical temp, it also has 86 columns corresponding to 86 possible elements
### and whether the element listed in the column is in material or not.
### It has duplicates.
### (7) unique_m: This is m with the duplicates removed. It has no duplicates.
###
###
### Save the data for future analysis:
###
save.image("processed_data_production_9.RData")
###
### The file size is around 1.215 MB.
###
###
### This is used for checking. These should be deleted from
### the local directory.
###
write.csv(dat, "dat.csv")
write.csv(m, "m.csv")
write.csv(unique_m, "unique_m.csv")
######################################################################################
######################################################################################
######################################################################################
###
### In this part, I will just visually inspect the data and make some summaries.
###
###
### First let's look at the data I kept.
###
###
### Set up first:
###
library(CHNOSZ)
setwd("C:/Users/proud/Documents/RESEARCH/PROJECT - Superconductivity/Productions/Production_9")
load("processed_data_production_9.RData")
###
### Let's count the number of elements:
###
###
### This function just counts:
###
count_it = function(x)
{
sum(x > 0)
}
###
### This is the table that has the proportions.
### Note we just count columns 1:86. 87 & 88 have
### critical temp and formula name.
###
p_table = apply(unique_m[,1:86], 2, count_it)/nrow(unique_m)
names(p_table) = colnames(unique_m[,1:86])
non_zero_p_table = p_table[which(p_table > 0)]
non_zero_p_table = sort(non_zero_p_table, decreasing = TRUE)
###
### Now plot it:
###
off_set = 0.015
default_mar = c(5, 4, 4, 2) + 0.1 # c(bottom, left, top, right)
### mar c(bottom, left, top, right) = default is c(5, 4, 4, 2) + 0.1
par(mar = c(2, 5, 2, 1) + 0.1)
x = 1:length(non_zero_p_table)
plot( x = x, y = non_zero_p_table, xaxt = "n", xlab = "",
ylab = "Element Proportion", pch = 19,
cex.lab = 1.25, ylim = c(0,max(non_zero_p_table) + off_set * 1.05), cex = 0.65)
abline(h = seq(from = 0, to = 0.6, by = 0.05), lwd = 0.5, col = "grey")
text(x, non_zero_p_table+off_set, names(non_zero_p_table), cex = 0.65)
###
### The plot above is saved in "element_proportions.pdf".
### I did have to resize the plot.
###
###
### I'll also create a top 20 proportion too:
###
non_zero_p_table_top_20 = non_zero_p_table[1:20]
x = 1:length(non_zero_p_table_top_20)
plot( x = x, y = non_zero_p_table_top_20, xaxt = "n", xlab = "",
ylab = "Element Proportion", pch = 19,
cex.lab = 1.25, ylim = c(0,max(non_zero_p_table_top_20) + off_set * 1.05), cex = 0.65)
abline(h = seq(from = 0, to = 0.6, by = 0.05), lwd = 0.5, col = "grey")
text(x, non_zero_p_table_top_20 + off_set* 1.1, names(non_zero_p_table_top_20))
###
### The plot above is saved in "element_proportions_top_20.pdf".
### I did have to resize the plot.
###
###
### Let's save a csv as well
###
write.csv(non_zero_p_table, "non_zero_p_table.csv")
###
### Now collect summary data per element.
### ncol(m) - 2 : This is the number of elements
### 1:86 : Leave out 87 which the formula and 88 which is the temp
### 7 : 7 parameters used.
###
summary_matrix = matrix(NA, ncol(unique_m) - 2, 7)
rownames(summary_matrix) = colnames(unique_m)[1:86]
colnames(summary_matrix) = c("Min", "Q1", "Med", "Q3", "Max", "Mean", "SD")
for ( j in 1:nrow(summary_matrix) )
{
tmp = unique_m[ which(unique_m[,j] > 0), "critical_temp"]
summary_matrix[j,1] = min(tmp)
summary_matrix[j,2] = quantile(tmp, prob = 0.25, names = F)
summary_matrix[j,3] = median(tmp)
summary_matrix[j,4] = quantile(tmp, prob = 0.75, names = F)
summary_matrix[j,5] = max(tmp)
summary_matrix[j,6] = mean(tmp)
summary_matrix[j,7] = sd(tmp)
}
###
### Order by mean & Now plot the means:
###
summary_matrix = summary_matrix[ order(summary_matrix[,6], decreasing = TRUE) , ]
summary_matrix = as.data.frame(summary_matrix)
###
### The last 9 lines give non-sense results.
### These are mostly noble gases. Remove them:
###
summary_matrix = na.omit(summary_matrix)
###
### Now plot them:
###
mean_crit_temp_per_element = summary_matrix[,"Mean"]
off_set = 2
### mar c(bottom, left, top, right) = default is c(5, 4, 4, 2) + 0.1
par(mar = c(2, 5, 2, 1) + 0.1)
x = 1:length(mean_crit_temp_per_element)
plot( x = x, y = mean_crit_temp_per_element, xaxt = "n", xlab = "", ylab = "Mean Critical Temperature (K)",
, pch = 19, cex.lab = 1.25, ylim = c(-5, max(mean_crit_temp_per_element) + off_set * 1.1) + 5, cex = 0.65)
abline(h = seq(from = 0, to = 80, by = 10), lwd = 0.1, col = "grey")
text(x, mean_crit_temp_per_element + off_set, rownames(summary_matrix), cex = 0.65)
###
### The file above is saved in "mean_crit_temp_per_element.pdf".
###
###
### Look at the top 20 as well.
###
### mar c(bottom, left, top, right) = default is c(5, 4, 4, 2) + 0.1
windows(width = 10, height = 7)
par(mar = c(2, 5, 2, 1) + 0.1)
x = 1:length(mean_crit_temp_per_element[1:20])
plot( x = x, y = mean_crit_temp_per_element[1:20], xaxt = "n", xlab = "", ylab = "Mean Critical Temperature (K)",
, pch = 19, cex.lab = 1.25, ylim = c(35, 85), cex = 0.65)
abline(h = seq(from = 35, to = 85, by = 5), lwd = 0.1, col = "grey")
text(x, mean_crit_temp_per_element[1:20] + 2, rownames(summary_matrix)[1:20])
###
### The file above is saved in "mean_crit_temp_per_element_top_20.pdf".
###
###
### Now repeat but sort by standard deviation:
###
summary_matrix = summary_matrix[ order(summary_matrix[,7], decreasing = TRUE) , ]
summary_matrix = as.data.frame(summary_matrix)
###
### Now plot it:
###
sd_crit_temp_per_element = summary_matrix[,"SD"]
off_set = 2
### mar c(bottom, left, top, right) = default is c(5, 4, 4, 2) + 0.1
par(mar = c(2, 5, 2, 1) + 0.1)
x = 1:length(sd_crit_temp_per_element)
plot( x = x, y = sd_crit_temp_per_element, xaxt = "n", xlab = "", ylab = "SD Critical Temperature (K)",
, pch = 19, cex.lab = 1.25, ylim = c(-5, max(sd_crit_temp_per_element) + off_set * 1.1) + 5, cex = 0.65)
abline(h = seq(from = 0, to = 80, by = 5), lwd = 0.1, col = "grey")
text(x, sd_crit_temp_per_element + off_set, rownames(summary_matrix), cex = 0.65)
###
### The plot is saved in "sd_crit_temp_per_element.pdf".
###
###
### Look at the top 20 as well.
###
### mar c(bottom, left, top, right) = default is c(5, 4, 4, 2) + 0.1
windows(width = 10, height = 7)
par(mar = c(2, 5, 2, 1) + 0.1)
x = 1:length(sd_crit_temp_per_element[1:20])
plot( x = x, y = sd_crit_temp_per_element[1:20], xaxt = "n", xlab = "", ylab = "SD Critical Temperature (K)",
, pch = 19, cex.lab = 1.25, ylim = c(30,45), cex = 0.65)
abline(h = seq(from = 30, to = 45, by = 5), lwd = 0.1, col = "grey")
text(x, sd_crit_temp_per_element[1:20] + 0.75, rownames(summary_matrix)[1:20])
###
### The plot is saved in "sd_crit_temp_per_element_top_20.pdf".
###
###
### Now plot the mean and sd
###
windows(width = 9, height = 5)
par(mfrow=c(1,2), mar = c(5,5,3,1) + 0.1)
plot(SD ~ Mean , data = summary_matrix,
xlab = "Mean Critical Temperature (K)", ylab = "SD Critical Temperature (K)", pch = 19, cex.lab = 1)
plot(SD ~ log(Mean) , data = summary_matrix,
xlab = "log(Mean Critical Temperature) (K)", ylab = "SD Critical Temperature (K)", pch = 19, cex.lab = 1)
par(mfrow=c(1,1))
###
### The picture is saved in "sd_vs_mean_crit_temp.pdf".
###
###
### Let's look at the histogram of the critical temp for the data selected
###
par(mfrow = c(1,3))
hist(unique_m$critical_temp, col = "grey", freq = FALSE, xlab = "Critical Temperature (K)", main = "")
boxplot(unique_m$critical_temp, col = "grey", xlab = "Critical Temperature (K)", horizontal = TRUE, pch = 19)
plot(rev(sort(unique_m$critical_temp)), cex = 0.65, pch = 19, ylab = "Critical Temperature (K)", ylim = c(0,200))
abline(h = 0 , lwd = 0.1, lty = 2)
par(mfrow = c(1,1))
###
### The above is saved in "hist_boxplot_crit_temp.pdf".
###
hist(unique_m$critical_temp, col = "grey", freq = FALSE,
xlab = "Critical Temperature (K)", main = "")
###
### The above is saved in "hist_crit_temp.pdf".
###
###
### Let's look at the histogram of critical temp of the rejected
### material.
###
rejected_temps = dat[which(reject == 1), "critical_temp"]
hist(rejected_temps, col = "grey", freq = FALSE, xlab = "Critical Temperature (K)", main = "")
###
### The above is saved in "hist_rejected_crit_temp.pdf".
###
###
### Let's compare the quantiles of the two distributions:
###
quantiles_material_kept = quantile(unique_m$critical_temp, prob = seq(from = 0.01, to = 0.99, by = 0.01), names = F)
quantiles_material_rejected = quantile(rejected_dat$critical_temp, prob = seq(from = 0.01, to = 0.99, by = 0.01), names = F)
par(mar = c(5,5,3,1) + 0.1)
plot(quantiles_material_kept ~ quantiles_material_rejected, pch = 19,
xlab = "Quantiles of Rejected", ylab = "Quantiles of Kept")
abline(a = 0, b = 1, col = "red", lty = 2)
###
### The above is saved in "quantiles_of_kept_vs_rejected.pdf".
###
###
### This does not make it look like they are the same; Critical temps are higher for
### what I have kept!
###
###
### Compare summary measures:
###
round(summary(unique_m$critical_temp),2)
round(summary(rejected_dat$critical_temp),2)
### > round(summary(unique_m$critical_temp),2)
### Min. 1st Qu. Median Mean 3rd Qu. Max.
### 0.00 5.37 20.00 34.42 63.00 185.00
### > round(summary(rejected_dat$critical_temp),2)
### Min. 1st Qu. Median Mean 3rd Qu. Max.
### 0.09 1.36 3.43 12.58 12.50 135.00
###
### Actually, it looks like I am leaving some low temp ones out!
###
###
### Let's look at some values at random:
###
set.seed(8811)
rejected_dat[ sample( 1:nrow(rejected_dat), size = 5, replace = F), ]
###
### I could see some mistakes: D0.018Nb0.982 = what is D?
### Pu1Co1Ga5 has Plutonium, etc.
###
###
### Let's do some clean up:
###
rm(j)
rm(tmp)
rm(x)
###
### Save results up to summary analysis:
###
save.image("visual_and_summary_analysis.RData")
###################################################################################
###################################################################################
###################################################################################
###
### In this next part, I will create the training data.
###
###
### This function is the new definition of standard deviation.
### It is the population sd.
###
### I had some problems with R's sd function.
###
std = function(x)
{
out = sqrt(mean((x - mean(x))^2))
out
}
#############################################################################
###
### This function gets the basic summaries based
### on one variable.
### Note that I will be ignoring the missing values.
###
### y = some property of material such as the atomic mass
### p = proportion or coefficient in the chemical formula
###
get_features = function(y, p)
{
###
### out = index to remove the missing values.
###
out = -which(is.na(y))
if ( length(out) > 0 ) {
new_y = y[out]
new_p = p[out]
} else {
new_y = y
new_p = p
}
###
### Now get the features after NA's have been removed.
###
###
### I also have added absolute value for some measurements because
### some values may be negative.
###
mean_y = mean(new_y)
wtd_mean_y = sum(new_p * new_y)
gmean_y = exp(mean(log(abs(new_y))))
wtd_gmean_y = exp(sum(new_p * log(abs(new_y))))
tmp = abs(new_y)/sum(abs(new_y))
entropy_y = -1 * sum(tmp * log(tmp))
tmp = (new_p * abs(new_y))/sum(new_p * abs(new_y))
wtd_entropy_y = -1 * sum(tmp * log(tmp))
range_y = max(new_y) - min(new_y)
wtd_range_y = max(new_p * new_y) - min(new_p * new_y)
lrange_y = max(log(abs(new_y))) - min(log(abs(new_y)))
wtd_lrange_y = max(new_p * log(abs(new_y))) - min(new_p * log(abs(new_y)))
std_y = std(new_y)
wtd_std_y = sqrt(sum((new_y - wtd_mean_y)^2*new_p))
out = c(mean_y, wtd_mean_y, gmean_y, wtd_gmean_y, entropy_y,
wtd_entropy_y, range_y, wtd_range_y, std_y, wtd_std_y)
out
}
#############################################################################
###
### This is the function that will extract information
###
### x = formula of the material
### ed = element data file. "ed" **MUST** have a column name "Element".
###
extract = function(x, ed)
{
###
### Note that names(ratios) gives the
### listing of the elements.
###
### ratios here, tells us how many of each element is in the
### material formula. These are basically the coefficients
### in front of the elements in the chemical formula.
###
ratios = makeup(x)
prob_ratios = data.frame( prob_ratios = ratios/sum(ratios), Element = names(ratios))
###
### Here's the subset table I create:
###
subset_table = subset(ed, Element %in% names(ratios),
c(Element, AtomicMass, FirstIonizationEnergy, AtomicRadius, Density, ElectronAffinity,
FusionHeat, ThermalConductivity, Valence) )
###
### Merge with the proportons. Very important: the column "Element"
### must be present in both subset_table and prob_ratios!
###
subset_table = merge(subset_table, prob_ratios)
###
### Atomic Mass:
###
AtomicMass_features = get_features(subset_table[,"AtomicMass"], subset_table[,"prob_ratios"])
###
### First Ionization Energy = FIE
###
### Note FIE is highly correlated with Electronegativity
###
FirstIonizationEnergy_features = get_features(subset_table[,"FirstIonizationEnergy"],
subset_table[,"prob_ratios"])
###
### AtomicRadius
### Note: AtomicRadius & CovalentRadius are highly correlated.
###
AtomicRadius_features = get_features(subset_table[,"AtomicRadius"],
subset_table[,"prob_ratios"])
###
### Density
###
Density_features = get_features(subset_table[,"Density"],
subset_table[,"prob_ratios"])
###
### ElectronAffinity
### Note: A small constant 1.5 should have been added to prevent log(0) = -Inf
###
ElectronAffinity_features = get_features(subset_table[,"ElectronAffinity"],
subset_table[,"prob_ratios"])
###
### FusionHeat
###
FusionHeat_features = get_features(subset_table[,"FusionHeat"],
subset_table[,"prob_ratios"])
###
### ThermalConductivity
###
ThermalConductivity_features = get_features(subset_table[,"ThermalConductivity"],
subset_table[,"prob_ratios"])
###
### Valence
###
Valence_features = get_features(subset_table[,"Valence"],
subset_table[,"prob_ratios"])
###
### Output
###
number_of_elements = length(ratios)
out = c(number_of_elements, AtomicMass_features, FirstIonizationEnergy_features,
AtomicRadius_features, Density_features, ElectronAffinity_features,
FusionHeat_features, ThermalConductivity_features, Valence_features)
return(out)
}
#############################################################################
###
### There will be two train data; This first one has the iron and cuprate
### 81 is the initial number of features extracted.
### 82 will be whether iron is in there.
### 83 will be whether it is cuprate.
### 84 will be the response.
###
### train: This is the main file I will use to create the models.
###
train = matrix(0, nrow(unique_m), 84)
colnames(train) = c(
"number_of_elements",
"mean_atomic_mass",
"wtd_mean_atomic_mass",
"gmean_atomic_mass",
"wtd_gmean_atomic_mass",
"entropy_atomic_mass",
"wtd_entropy_atomic_mass",
"range_atomic_mass",
"wtd_range_atomic_mass",
"std_atomic_mass",
"wtd_std_atomic_mass",
"mean_fie",
"wtd_mean_fie",
"gmean_fie",
"wtd_gmean_fie",
"entropy_fie",
"wtd_entropy_fie",
"range_fie",
"wtd_range_fie",
"std_fie",
"wtd_std_fie",
"mean_atomic_radius",
"wtd_mean_atomic_radius",
"gmean_atomic_radius",
"wtd_gmean_atomic_radius",
"entropy_atomic_radius",
"wtd_entropy_atomic_radius",
"range_atomic_radius",
"wtd_range_atomic_radius",
"std_atomic_radius",
"wtd_std_atomic_radius",
"mean_Density",
"wtd_mean_Density",
"gmean_Density",
"wtd_gmean_Density",
"entropy_Density",
"wtd_entropy_Density",
"range_Density",
"wtd_range_Density",
"std_Density",